Scaling Up Structural Clustering to Large Probabilistic Graphs Using Lyapunov Central Limit Theorem
Summary: Recasts expected Jaccard similarity in probabilistic-graph structural clustering as a one-tailed Normal CDF via a Lyapunov CLT-based construction of random variables. Achieves linear runtime (vs prior quadratic DP), enabling web-scale clustering of massive graphs. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Joseph Howie
- 2. Venkatesh Srinivasan
- 3. Alex Thomo
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| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,159 | k-Nearest Neighbors in Uncertain Graphs | 2010 | VLDB | 0.00013584223 |
| 1,448 | Distance-Constraint Reachability Computation in Uncertain Graphs | 2011 | VLDB | 0.00011928845 |
| 3,106 | Injecting Uncertainty in Graphs for Identity Obfuscation | 2012 | VLDB | 7.5550946e-05 |
| 3,622 | Efficient and Effective Algorithms for Clustering Uncertain Graphs | 2019 | VLDB | 6.9021704e-05 |
| 4,654 | Dynamic Structural Clustering on Graphs | 2021 | SIGMOD | 6.0129362e-05 |
| 6,522 | Clustering Uncertain Graphs | 2018 | VLDB | 5.0230003e-05 |
| 7,903 | Coarsening Massive Influence Networks for Scalable Diffusion Analysis | 2017 | SIGMOD | 4.6170606e-05 |
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